"KMeans-Clusterting"

Question

Good morning,

I'm using the KMeans-Cluster Model to merge similar measurement values in one group (cluster).

The process is quite similar to the Help-tutorial step 4 KMeans-Model with Iris-data.

I have 10 different attributes, but it seems, KMeans Methode merge measurement values especially
depending on the first 3 attributes.

Each attribute should be the same weight at this method, right?

Maybe you had the same experience, or could give me an advice.

Are there some settings to change for the process?

Thanks.

Tammi · Answer

Hi Andrew,

thanks a lot for the hint. In my case the value range of the first 2 attributes are very wide in comparison 
to the other range of attributes.

Thanks.

Have a nice day.

Tammi

Andrew2 · Answer

Hello Tammi, The attributes' range has an effect on its influence on the clusters found. In the example that follows, I normalise the first three attributes but not the fourth and I observe that the clustering improves on the iris data set. For your problem, it's a question for the domain expert to know whether the ranges should be changed or not. regards Andrew

In many cases, no target attribute (label) can be defined and the data should be automatically grouped. This procedure is called "Clustering". RapidMiner supports a wide range of clustering schemes which can be used in just the same way like any other learning scheme. This includes the combination with all preprocessing operators.

In this experimen, the well-known Iris data set is loaded (the label is loaded, too, but it is only used for visualization and comparison and not for building the clusters itself). One of the most simple clustering schemes, namely KMeans, is then applied to this data set. Afterwards, a dimensionality reduction is performed in order to better support the visualization of the data set in two dimensions.

Just perform the process and compare the clustering result with the original label (e.g. in the plot view of the example set). You can also visualize the cluster model itself.

Tammi · Answer

Hi haddock, thanks for the fast response. I agree, you have to be careful about the results of the data process. What will be expected - and the real results. In my case, the values of the first 3 attributes are very different. The values of the other attributes are more "similar". This could be a reason for the cluster process. Anyway it would be great if the process also make some clusters in relation to the attritbutes 4 - 10. Have a nice weekend.

In many cases, no target attribute (label) can be defined and the data should be automatically grouped. This procedure is called "Clustering". RapidMiner supports a wide range of clustering schemes which can be used in just the same way like any other learning scheme. This includes the combination with all preprocessing operators.

In this experimen, the well-known Iris data set is loaded (the label is loaded, too, but it is only used for visualization and comparison and not for building the clusters itself). One of the most simple clustering schemes, namely KMeans, is then applied to this data set. Afterwards, a dimensionality reduction is performed in order to better support the visualization of the data set in two dimensions.

Just perform the process and compare the clustering result with the original label (e.g. in the plot view of the example set). You can also visualize the cluster model itself.